Comparison of Key Indicators and Evaluation Models of Soil and Water Loss Sensitivity in Karst Ecosystem[J].Research of Soil and Water Conservation,2021,28(02):256-266.
岩溶生态系统水土流失敏感性关键指标和评估模型比较
- Title:
- Comparison of Key Indicators and Evaluation Models of Soil and Water Loss Sensitivity in Karst Ecosystem
- 文章编号:
- 1005-3409(2021)02-0256-11
- Keywords:
- karst ecosystem; evaluation of soil and water loss sensitivity; key indicators; spatial principal component analysis; 3S technology
- 分类号:
- S157
- 文献标志码:
- A
- 摘要:
- 为了能更合理地评价岩溶地区水土流失敏感性,以岩溶生态系统为研究对象,以修正通用土壤流失模型(RUSLE)为蓝本,通过引入石漠化因子,对RUSLE进行修正; 通过计算土壤侵蚀量、划分土壤侵蚀强度、水土流失关键因子影响评估,构建广西岩溶生态系统水土流失敏感性评估指标体系; 在此基础上,对比分析了空间叠加分析法、层次分析法、空间主成分分析法3种水土流失敏感性评价方法,利用遥感数据对多种评估结果进行验证与对比,选取最优评价方法对广西岩溶生态系统水土流失敏感性进行了评估。结果表明:引入石漠化因子后的修正通用土壤流失模型(RUSLE)可以较合理地评价广西岩溶生态系统土壤流失空间分布状况; 广西岩溶生态系统水土流失敏感性因子作用大小顺序为:气象>石漠化>植被>土壤>地形>人类活动,土壤流失影响率与大多数敏感性因子和土壤侵蚀强度均呈一致变化趋势; 3种水土流失敏感性评价方法对比以主成分分析法(PCAEI)评估结果最优,层次分析法和加权综合评价法(AHPEI)次之,因子叠加分析法(CMSEI)较差。广西岩溶生态系统水土流失敏感性以轻度、中度、高度敏感区为主,水土流失敏感性整体中度偏重,水土流失敏感性强度由东部向西部、由南部向北部逐渐增强,敏感区主要分布在河池市、百色市,不敏感区主要分布在桂林市、崇左市。
- Abstract:
- In order to evaluate the sensitivity of soil and water loss in karst more reasonably, the soil loss model of karst ecosystem was established based on the modified RUSLE model, into which karst rocky desertification correction factor was introduced. Based on the ratio of soil erosion amount to soil erosion area under different levels of intensity, the assessment model of impact of key factors of soil and water loss in karst ecosystem was established, and the key indicators of soil and water loss sensitivity in karst ecosystem in Guangxi were analyzed. With the support of 3S technology, an assessment indicator system of soil and water loss sensitivity in karst ecosystem in Guangxi was established. Spatial superposition analysis method, analytic hierarchy process and spatial principal component analysis method were used to establish the assessment models of soil and water loss sensitivity in karst ecosystem in Guangxi. The remote sensing monitoring data of soil and water loss were used to verify and compare the evaluation models, and the optimal model was chosen to analyze the spatial differentiation characteristics of soil and water loss sensitivity in karst ecosystem in Guangxi. The results show that the key indicators of soil and water loss sensitivity in karst ecosystem in Guangxi follow the order meteorology>rocky desertification>vegetation>soil>topography>human activities; the PCAEI model is the best, followed by the AHPEI model and the CMSEI model comes to the next. From the grade of sensitivity, the sensitive areas to soil and water loss were mainly mild, moderate and highly sensitive areas. On the whole, the risk of soil and water loss sensitivity in karst ecosystem in Guangxi was severe. From the perspective of spatial distribution, the intensity of soil and water loss sensitivity gradually increased from the east to the west and from the south to the north in the karst areas, and sensitive areas mainly distributed in Hechi City and Baise City, while insensitive areas mainly distributed in Guilin City and Chongzuo City.
参考文献/References:
[1] Wischmeier W H, Smith D D. Predicting rainfall erosion losses: a guide to conservation planning[M]. Washington DC: USDA Agricultural Handbook,1978.
[2] Laflen J M, Lwonard J L, Foster G R. WEPP a new generation of erosion prediction technology[J]. Journal of Soil and Water Conservation,1991,46(1):34-38.
[3] Morgan R P C, Quinton J N, Smith R E, et al. The European Soil Erosion Model(EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments[J]. Earth Surface Processes & Landforms,2015,23(6):527-544.
[4] Beasley D B, Huggins L F, Monke E J. ANSWERS: A Model for Watershed Planning[J]. Transactions of the ASAE,1980,23(4):938-944.
[5] Baffalt C, Nearing M A, Nicks A D. Impact of GLIGEN parameters on WEPP predicted average soil loss[J]. Transactions of the Asae,1996, 39(2):1001-1020.
[6] 刘宝元,谢云,张科利.土壤侵蚀预报模型[M].北京:中国科学技术出版社,2001.
[7] 汤立群.流域产沙模型的研究[J].水科学进展,1996,7(1):47-53.
[8] 蔡强国,王贵平,陈永宗.黄土高原小流域侵蚀产沙过程与模拟[M].北京:科学出版社,1998.
[9] 蔡强国,刘纪根,刘前进.岔巴沟流域次暴雨产沙统计模型[J].地理研究,2004,23(4):433-439.
[10] 蔡强国,袁再健,程琴娟,等.分布式侵蚀产沙模型研究进展[J].地理科学进展,2006,25(3):48-54.
[11] 崔普伟.基于单元流域的黄土丘陵沟壑区岔巴沟流域次暴雨产沙经验模型研究[D].武汉:华中农业大学,2010.
[12] 庞靖鹏,刘昌明,徐宗学.基于SWAT模型的径流与土壤侵蚀过程模拟[J].水土保持研究,2007,14(6):88-93.
[13] Baffalt C, Nearing M A, Nicks A D. Impact of GLIGEN parameters on WEPP predicted average soil loss[J]. Transactions of the Asae,1996, 39(2):1001-1020.
[14] 曾成.基于GIS和RUSLE的典型喀斯特地区土壤侵蚀演变与空间要素关联性分析[D].贵阳:贵州师范大学,2018.
[15] 高江波,左丽媛,王欢.喀斯特峰丛洼地生态系统服务空间权衡度及其分异特征[J].生态学报,2019,39(21):7829-7839.
[16] 陈龙跃,张雨,汪权方,等.鄂渝喀斯特山区土壤侵蚀特征及主影响因子识别分析[J].湖北大学学报:自然科学版,2020,42(2):172-178,184.
[17] 任青青,严友进,甘艺贤,等.短历时强降雨对典型喀斯特坡耕地侵蚀产沙的影响[J].水土保持学报,2019,33(6):105-112.
[18] 张芳挺,熊康宁,陈浒,等.喀斯特高原山地水土流失影响因素及生态效益评价:以贵州毕节石桥小流域为例[J].水土保持研究,2009,16(5):88-92,97.
[19] 傅瓦利,贾红杰,张文晖,等.岩溶山区土壤耕作侵蚀研究:以重庆市中梁山为例[J].中国岩溶,2006,25(2):152-156.
[20] 高华端,李锐.喀斯特地区原状土的可蚀性[J].中国水土保持科学,2007,5(5):1-4.
[21] 裴建国,李庆松.典型岩溶峰丛山区土地利用与水土流失[J].水土保持通报,2006,26(2):94-99.
[22] 吴秀芹,蔡运龙,蒙吉军.喀斯特山区土壤侵蚀与土地利用关系研究:以贵州省关岭县石板桥流域为例[J].水土保持研究,2005,12(4):46-48,77.
[23] 李品荣,孟广涛,李国昌.不同土地利用方式下土壤地力变化和水土流失状况研究[J].水土保持研究,2009,16(3):95-99.
[24] 王小丹,钟祥浩,范建容.西藏水土流失敏感性评价及其空间分异规律[J].地理学报,2004,59(2):183-188.
[25] 李大龙,杨井,李卫红,等.基于GIS和USLE的伊犁河谷土壤侵蚀敏感性评价[J].生态学杂志,2016,35(4):942-951.
[26] 蔡文博,蔡永立.基于GIS方法的泸沽湖流域水土流失敏感性评价[J].水土保持研究,2014,21(3):79-83,2.
[27] 孙小涛,周忠发,陈全,等.重点生态功能区水土流失敏感性评价与分布研究:以贵州省雷山县为例[J].水土保持学报,2016,30(6):73-78,133.
[28] 杨新华,王侃,李加林,等.基于GIS的慈溪市土壤侵蚀敏感性评价[J].水土保持通报,2016,36(4):210-215.
[29] 陈盼盼,胡利利,李亦秋,等.龙门山地区水土流失敏感性评价及其空间分异[J].水土保持通报,2017,37(3):237-241.
[30] 张景华,高慧,欧阳渊,等.贵州省黔西县土壤侵蚀敏感性评价[J].中国水土保持科学,2018,16(2):88-94.
[31] 徐萍.基于GIS和RULSE模型对龙江县的水土流失敏感性分析[J].哈尔滨师范大学自然科学学报,2019,35(3):101-105.
[32] 王娇,程维明,祁生林,等.基于USLE和GIS的水土流失敏感性空间分析:以河北太行山区为例[J].地理研究,2014,33(4):614-624.
[33] 王红岩,李强子,吴利桥,等.基于RS和GIS的水土流失敏感性评价及动态监测[J].水土保持研究,2015,22(2):64-68.
[34] 马玉新.辽西荒漠化区域水土流失敏感性评价:以彰武县衙门小流域为例[J].黑龙江水利科技,2018,46(4):218-221.
[35] 罗俊,王克林,陈洪松,等.桂西北喀斯特地区水土流失敏感性评价[J].长江流域资源与环境,2009,18(6):579-584.
[36] 凡非得,王克林,熊鹰,等.西南喀斯特区域水土流失敏感性评价及其空间分异特征[J].生态学报,2011,31(21):6353-6362.
[37] 陈燕丽,莫建飞,莫伟华,等.近30年广西喀斯特地区石漠化时空演变[J].广西科学,2018,25(5):625-631.
[38] 国家环境保护总局.生态保护红线划定技术指南[M].北京:国家环境保护总局,2015.
[39] 王魁.典型岩溶小流域水土流失监测体系研究[D].南宁:广西大学,2013.
[40] 张恩伟,彭双云,冯华梅.基于GIS和RUSLE的滇池流域土壤侵蚀敏感性评价及其空间格局演变[J].水土保持学报,2020,34(2):115-122.
[41] 路中,雷国平,王居午,等.30年来东北典型黑土区水土流失敏感性时空分异特征研究[J].自然灾害学报,2019,28(4):186-195.
[42] 郭泽呈,魏伟,庞素菲,等.基于SPCA和遥感指数的干旱内陆河流域生态脆弱性时空演变及动因分析:以石羊河流域为例[J].生态学报,2019,39(7):296-310.
[43] 邓辉,何政伟,陈晔,等.信息量模型在山地环境地质灾害危险性评价中的应用:以四川泸定县为例[J].自然灾害学报,2014,23(2):67-75.
[44] 韩富伟,张柏,宋开山.长春市土壤侵蚀潜在危险度分级及侵蚀背景的空间分析[J].水土保持学报,2007,21(1):39-43.
[45] 莫建飞,钟仕全,陈燕丽,等.极端降水事件下广西流域洪涝社会经济暴露度分析[J].灾害学,2018,33(2):83-88.
[46] 谢坤坚,卢远,蔡卓杰,等.广西降雨侵蚀力时空变化分析[J].中国水土保持,2016(12):50-53,69.
[47] 李阳兵,邵景安,王世杰,等.基于岩溶生态系统特性的水土流失敏感性评价[J].山地学报,2007,25(6):671-677.
[48] 蔡雄飞,王济,雷丽,等.不同雨强对我国西南喀斯特山区土壤侵蚀影响的模拟研究[J].水土保持学报,2009,23(6):5-8,13.
备注/Memo
收稿日期:2020-03-23 修回日期:2020-05-06
资助项目:广西科技厅重点研发项目(桂科AB17292051); 广西气象局重点项目(桂气科2019Z04)
第一作者:莫建飞(1978—),男(壮族),广西象州人,硕士,高级工程师,主要从事生态遥感监测与评估应用研究。E-mail:mojfei@163.com
通信作者:莫伟华(1968—),女,广西宾阳人,硕士,正研级高级工程师,主要从事生态气象和环境遥感研究。E-mail:mwh-0419@163.com